Reference : Adding value to test-day data by using modified best prediction method
Scientific congresses and symposiums : Paper published in a book
Life sciences : Animal production & animal husbandry
Life sciences : Genetics & genetic processes
Adding value to test-day data by using modified best prediction method
Gillon, Alain mailto [Université de Liège - ULiège > Sciences agronomiques > Zootechnie >]
Abras, Sven [Association Wallonne de l'Elevage > > > >]
Mayeres, Patrick [Association Wallonne de l'Elevage > > > >]
Bertozzi, Carlo [Association Wallonne de l'Elevage > > > >]
Gengler, Nicolas mailto [Université de Liège - ULiège > Sciences agronomiques > Zootechnie >]
Book of Abstracts of the 60th Annual Meeting of the European Association for Animal Production
Wageningen Academic Publishers
The Netherlands
60th EAAP Annual Meeting
August 24-27, 2009
[en] lactation yields computation ; modified best prediction ; test-day model ; management tools
[en] Computation of lactation yields from test-day yield has lost much of its importance for genetic evaluations as
<br />the use of test-day models is rather widespread. At the same time its importance for intra-farm management
<br />increases at farms as a base for advanced management tools. The most common official method to compute
<br />lactation yield is the Test Interval Method (TIM). Alternative methods for computing cumulated productions
<br />were developed. These methods can be considered as improvements of TIM as the interpolation method,
<br />or completely different methods as multiple-trait prediction (MTP) and best prediction (BP). Research
<br />in this field has shown the potential to compute lactation parameters (e.g., cumulated production) with
<br />test-day models. The aim of this study was to develop a new method which takes into account advantages
<br />and disadvantages of existing methods, and to test its potential to provide useful tools to help farmers to
<br />make management decisions. The second objective was to compare the accuracy and the robustness of this
<br />method with those of BP and TIM. Because of its similarities with BP, the method developed here was
<br />called mBP, for modified-BP. The main difference from BP is the definition of the standard lactation curve.
<br />To minimize bias, components of standard lactation curves proper to each herd are computed jointly with
<br />random individual effects. Recently a new version of mBP was tested that puts expectations of constant
<br />animal effects to observed average values using Bayesian prediction, a feature also used by MTP.
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